Knowledge base query is a process of recognizing from a knowledge base an analogous case that shares similar features to a target case, such as a given document, a person, an entity, or a computer file. The recognition of the analogous case may involve similarity detection that may include computing a similarity metric between the target case and a candidate case in the knowledge base and comparing the similarity metric to a threshold. The threshold may be determined based on the tradeoffs between hits, missed opportunities, false alarms and correct rejections. Other analogy detection may include hyperplane separation models that fit the data on one or another side of a separation plane or hyperplane in a multidimensional feature space. Analogy detection may also be used in analogy-based reasoning including hypothesis generation.
In analogy detection, the target case and the cases stored in the knowledge base may each be represented by a feature vector including a plurality of discrete features. Conventionally, these features are independent to each other, and characterize particular aspects of the target case or the stored case. The analogy detection may be performed by exhaustive search of all the independent features within all the stored cases in the knowledge base. Such a representation of a feature vector, however, may not adequately represent deeper information about interactions or dependencies among the features, such as within the context of the target case or a stored case. Additionally, due to the large feature space and case space in the knowledge base, the query process that relies on exhaustive search of all features among all stored cases may be less efficient, time-consuming, and computationally intensive. The present inventor has recognized that there remains a need for systems and methods for more efficient knowledge management.